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Titlebook: Recent Developments of Fuzzy Matrix Theory and Applications; Madhumangal Pal Book 2024 The Editor(s) (if applicable) and The Author(s), un

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21#
發(fā)表于 2025-3-25 04:27:15 | 只看該作者
Matrices of Triangular Fuzzy Numbers, classified as: triangular fuzzy number (TFN), trapezoidal fuzzy number, hexagonal fuzzy number, octagonal fuzzy number, etc. The triangular fuzzy numbers (TFNs) are frequently used in different applications, as it is a very natural extension of real numbers.
22#
發(fā)表于 2025-3-25 11:30:15 | 只看該作者
23#
發(fā)表于 2025-3-25 14:08:20 | 只看該作者
Picture Fuzzy Matrices,nd . number of persons making negative comments about ., where .. Then the ratios . are called the degree of acceptance and degree of negation of the statement .. If ., then everybody clearly mentioned his/her opinion and the situation can be handled with the fuzzy set (FS) only. If ., then some peo
24#
發(fā)表于 2025-3-25 19:31:19 | 只看該作者
25#
發(fā)表于 2025-3-25 22:41:14 | 只看該作者
Fuzzy Matrices with Uncertain Rows and Columns,t, in many real-life applications, it is seen that columns and rows are not necessarily certain. For example, a fuzzy graph can be represented as a fuzzy matrix called an adjacent matrix. In the adjacent matrix for each vertex, there is a column and a row, e.g. for the .th vertex, there is .th row a
26#
發(fā)表于 2025-3-26 00:47:03 | 只看該作者
27#
發(fā)表于 2025-3-26 07:57:07 | 只看該作者
Picture Fuzzy Matrices,statement .. If ., then everybody clearly mentioned his/her opinion and the situation can be handled with the fuzzy set (FS) only. If ., then some people are not participated to make any comments about the statement ..
28#
發(fā)表于 2025-3-26 10:27:13 | 只看該作者
29#
發(fā)表于 2025-3-26 16:41:43 | 只看該作者
Fuzzy Matrices,on and multiplication rules are the same as a Boolean matrix. Fuzzy matrices are used to model problems of many fields, e.g. fuzzy relations, fuzzy relational equations, pattern classification, knowledge-based systems, etc.
30#
發(fā)表于 2025-3-26 17:22:39 | 只看該作者
Interval-Valued Fuzzy Matrices,S is to replace crisp membership degrees within [0, 1] by interval in [0,1]. So, in IVFS, the membership degree is a subset of the closed interval [0,1]. For this set, the determination of membership degree becomes easy, but it increases the uncertainty.
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